Rule-based classification systems to classify discrete sets of data are often difficult and expensive to maintain, and often insufficient for tasks involving large, varying, and/or complex data sets. In some cases, these systems may be prone to failure if faced with data that varies or changes over time, or data that contains variations within the classes themselves. In some cases, rule-based algorithms designed in advance may be ineffective at classifying the live data. Also, the manual design of effective rule-based classifiers may become difficult as the classification options become more complex. Also, it may be difficult to identify the features in the source data that may be used for effective automatic classification of data. Thus, it is with respect to these considerations and others that the invention has been made.